PDF(Pubmed)
Abstract：
UNASSIGNED: Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, this study was performed to identify gender-specific characteristics associated with fatal occupational injuries among migrant workers; the interactions between these factors, were also analyzed. In addition, we compared the utility of explainable artificial intelligence (XAI) using SHapley Additive exPlanations (SHAP) with logistic regression (LR) and discuss caveats regarding its use.
UNASSIGNED: We analyzed national statistics for occupational injuries among migrant workers (n = 67,576) in South Korea between January 1, 2007, and September 30, 2018. We applied an extreme gradient boosting model and developed SHAP and LR models for comparison.
UNASSIGNED: We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established.
UNASSIGNED: Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.
UNASSIGNED: We analyzed national statistics for occupational injuries among migrant workers (n = 67,576) in South Korea between January 1, 2007, and September 30, 2018. We applied an extreme gradient boosting model and developed SHAP and LR models for comparison.
UNASSIGNED: We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established.
UNASSIGNED: Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.
摘要：
■职业伤害分析对于制定预防策略至关重要。然而,很少有研究从性别角度评估农民工的严重职业伤害。因此,使用一种新的分析方法，这项研究是为了确定与农民工致命性职业伤害相关的性别特征；这些因素之间的相互作用，也进行了分析。此外,我们比较了使用SHapley加法扩张（SHAP）和逻辑回归（LR）的可解释人工智能（XAI）的效用，并讨论了有关其使用的警告。
■我们分析了2007年1月1日至2018年9月30日韩国农民工（n=67,576）职业伤害的国家统计数据。我们应用了极端梯度增强模型，并开发了SHAP和LR模型进行比较。
■我们发现农民工在致命职业伤害方面存在明显的性别差异，同一职业的男性比女性有更高的死亡风险。这些性别差异表明，有必要对农民工进行针对性别的职业伤害预防干预措施，以降低死亡率。职业仅是女性农民工死亡的重要预测指标，护理工作的死亡风险最高。如果不按性别进行详细的针对特定工作的分析，就无法确定女工的职业死亡风险。本研究中确定的SHAP的主要优点是自动识别和分析相互作用，确定每个特征的相对贡献的能力，和高的整体性能。使用SHAP时的主要警告是不能建立因果关系。
■按性别分层的详细工作分析，以及考虑到移民工人性别的干预措施，有必要降低职业死亡率。XAI方法应被视为流行病学研究的补充分析方法，因为它克服了传统统计分析的局限性。
■我们分析了2007年1月1日至2018年9月30日韩国农民工（n=67,576）职业伤害的国家统计数据。我们应用了极端梯度增强模型，并开发了SHAP和LR模型进行比较。
■我们发现农民工在致命职业伤害方面存在明显的性别差异，同一职业的男性比女性有更高的死亡风险。这些性别差异表明，有必要对农民工进行针对性别的职业伤害预防干预措施，以降低死亡率。职业仅是女性农民工死亡的重要预测指标，护理工作的死亡风险最高。如果不按性别进行详细的针对特定工作的分析，就无法确定女工的职业死亡风险。本研究中确定的SHAP的主要优点是自动识别和分析相互作用，确定每个特征的相对贡献的能力，和高的整体性能。使用SHAP时的主要警告是不能建立因果关系。
■按性别分层的详细工作分析，以及考虑到移民工人性别的干预措施，有必要降低职业死亡率。XAI方法应被视为流行病学研究的补充分析方法，因为它克服了传统统计分析的局限性。
